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Evaluating And Improving The Seu Reliability Of Artificial Neural Networks Implemented In Sram-Based Fpgas With Tmr, Brittany Michelle Wilson
Evaluating And Improving The Seu Reliability Of Artificial Neural Networks Implemented In Sram-Based Fpgas With Tmr, Brittany Michelle Wilson
Theses and Dissertations
Artificial neural networks (ANNs) are used in many types of computing applications. Traditionally, ANNs have been implemented in software, executing on CPUs and even GPUs, which capitalize on the parallelizable nature of ANNs. More recently, FPGAs have become a target platform for ANN implementations due to their relatively low cost, low power, and flexibility. Some safety-critical applications could benefit from ANNs, but these applications require a certain level of reliability. SRAM-based FPGAs are sensitive to single-event upsets (SEUs), which can lead to faults and errors in execution. However there are techniques that can mask such SEUs and thereby improve the …
Dynamic Reconfigurable Real-Time Video Processing Pipelines On Sram-Based Fpgas, Andrew Elbert Wilson
Dynamic Reconfigurable Real-Time Video Processing Pipelines On Sram-Based Fpgas, Andrew Elbert Wilson
Theses and Dissertations
For applications such as live video processing, there is a high demand for high performance and low latency solutions. The configurable logic in FPGAs allows for custom hardware to be tailored to a specific video application. These FPGA designs require technical expertise and lengthy implementation times by vendor tools for each unique solution. This thesis presents a dynamically configurable topology as an FPGA overlay to deploy custom hardware processing pipelines during run-time by utilizing dynamic partial reconfiguration. Within the FPGA overlay, a configurable topology with a routable switch allows video streams to be copied and mixed to create complex data …